Ahead knowledge of link quality can reduce the energy consumption of
wireless sensor networks. In this paper, we propose a cloud reasoning-based link
quality prediction algorithm for wireless sensor networks. A large number of link
quality samples are collected from different scenarios, and their RSSI, LQI, SNR
and PRR parameters are classified by a self-adaptive Gaussian cloud transformation
algorithm. Taking the limitation of nodes’ resources into consideration, the Apriori algorithm is applied to determine association rules between physical layer and
link layer parameters. A cloud reasoning algorithm that considers both short- and
long-term time dimensions and current and historical cloud models is then proposed
to predict link quality. Compared with the existing window mean exponentially
weighted method, the proposed algorithm captures link changes more accurately,
facilitating more stable prediction of link quality.